activity
20192022
most citedMyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images

10 citations · 11 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2022

UGformer for Robust Left Atrium and Scar Segmentation Across Scanners

Tianyi Liu, Size Hou, Jiayuan Zhu +2

Thanks to the capacity for long-range dependencies and robustness to irregular shapes, vision transformers and deformable convolutions are emerging as powerful vision techniques of…

eess.IV202210 cited

MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images

Lei Li, Fuping Wu, Sihan Wang +29

Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardiu…

eess.IV20201 cited

Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention and Dynamic Resampling

Haochuan Jiang, Chengjia Wang, Agisilaos Chartsias +1

Automatic segmentation of multi-sequence (multi-modal) cardiac MR (CMR) images plays a significant role in diagnosis and management for a variety of cardiac diseases. However, the…

eess.IV2020

Semi-supervised Pathology Segmentation with Disentangled Representations

Haochuan Jiang, Agisilaos Chartsias, Xinheng Zhang +6

Automated pathology segmentation remains a valuable diagnostic tool in clinical practice. However, collecting training data is challenging. Semi-supervised approaches by combining…

cs.IR2019

Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation

Fajie Yuan, Xiangnan He, Haochuan Jiang +4

Session-based recommender systems have attracted much attention recently. To capture the sequential dependencies, existing methods resort either to data augmentation techniques or…